HTTP Interface and External Systems for Retail Chain

Data Characteristics for This Category

Retail chains prepare registration documents for biomedicine. Data primarily originates from internal drug batch management systems, store sales data, supplier qualification document repositories, and compliance department audit records. Data updates occur daily or weekly, driven by batch arrivals, sales changes, and regulatory updates. Document structures are mostly structured, including drug batch numbers, production dates, expiration dates, supplier information, and sales records. However, unstructured or semi-structured documents are also present, such as scanned supplier production licenses, drug inspection report PDFs, and policy interpretation documents from provincial and municipal drug administrations. Field naming and units show some non-standardization. For example, batch number formats vary, and expiration dates may be in "days," "months," or "years," requiring unified conversion.

Constraints from These Characteristics on "HTTP Interface and External Systems"

Diverse and frequently updated data sources from retail chains require FastGPT's HTTP interface to support efficient incremental synchronization. This avoids performance bottlenecks from full synchronization. Batch management systems and sales data are typically exposed via API interfaces, requiring Bearer Token or API Key for authentication. Unstructured documents, like inspection report PDFs, need ingestion via file upload interfaces (/v1/vector/uploadFile) or by specifying storage bucket paths. This relies on FastGPT's document parsing capabilities. Non-standardized field names and units mean data preprocessing is necessary after interface calls, or fields Mapping rules must be configured within FastGPT. This ensures data accuracy and consistency, for example, converting all expiration dates to "days." High-frequency updates to store sales data can increase instantaneous concurrent requests, demanding higher requirements for rateLimit and maxConnections parameter settings.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`800 characters`	Retail chain registration documents often contain detailed policy interpretations, requiring longer context for understanding.
`Chunk size`	`500 characters`	Ensures each segment contains sufficient information while preventing individual segments from being too long, which could affect recall efficiency.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Accommodates uploading large files like scanned inspection reports and production licenses.
`similarityThreshold`	`0.75`	Balances recall accuracy and quantity, reducing interference from irrelevant results.
`reRankTopN`	`Top 5 entries`	Reranks initial recall results to improve the relevance of the final answer.
`HTTP_TIMEOUT_SECONDS`	`60 seconds`	Addresses situations where external systems have large data volumes or slow responses, preventing requests from timing out prematurely.

Common Pitfalls

When calling the /v1/chat/completions interface, the returned choices list is empty or incomplete. This can happen if maxContext is set too low, preventing the model from getting enough context, or if similarityThreshold is too high, filtering out relevant but less similar document chunks.
After uploading a file via the HTTP interface, the document content is not found in the knowledge base. This often occurs if the UPLOAD_FILE_MAX_SIZE parameter limits the file size, causing large file uploads to fail, or if PARSE_FILE_TIMEOUT_SECONDS is too short, leading to large file parsing timeouts.
When concurrently calling external system APIs, a 429 Too Many Requests status code appears. This happens because the external system limits the request frequency for a single IP or API Key, and rateLimit or maxConnections are not set in FastGPT for traffic control.

Verification Steps

Upload a typical drug inspection report PDF file via the FastGPT administration interface. Observe if it is successfully parsed and generates questions and answers.
Configure an HTTP interface to connect to the retail chain's drug batch management system. Manually trigger a data synchronization and verify if corresponding drug batch information has been added to the FastGPT knowledge base.
Simulate multiple users simultaneously querying FastGPT for registration document information. Monitor backend logs to confirm that external system API calls do not result in 429 or 5xx errors.
Use FastGPT's test chat function to ask questions about different types of registration documents in the knowledge base (e.g., supplier qualifications, sales compliance policies). Evaluate the accuracy and completeness of the answers. Adjust similarityThreshold and reRankTopN parameters based on actual business needs.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.